During the refugee crises in Sweden 2015, it became clear that there are several disadvantages with the Swedish reception system. It is well known that refugees and other immigrants are not distributed equally among the country's municipalities. An even distribution is important for a successful reception and integration according to the Swedish Authorities and Regions. The purpose of this paper is to analyze what factors could be the reason for an uneven distribution of refugees in Sweden. To analyze the question, secondary data is used from 2006 to 2015 in order to make pooled regressions and Least Squares Dummy Variable (LSDV) models. Different data is collected that may be related to the dependent variable of new arrivals per capita based on previous studies and theories. In addition, we are aware that there are more aspects that can affect the municipal reception but cannot be measured in this study. The study found that the following investigated variables are associated with the number of arrivals received in a municipality. It was shown that the percentage of former foreign-born in the municipality had a positive correlation with the proportion of new arrivals and it also suggested that these new arrivals settle in areas with others from similar backgrounds. An unexpected result was the percentage of seniors in a community that affects the dependent variable positively. Additionally, the average income in a municipality shows a negative correlation, whereby this result could be explained from people with similar incomes living in similar areas. The study also analyzed the education level of people which shows a negative relationship that can be justified in a similar way. It was also found that unemployment affects new arrivals positively but the results are not clear.  It was found that housing deficits have a negative relationship with the dependent variable, whilst housing surplus has a positive correlation which was expected. Countryside municipalities show a positive result that is statistically significant and the metropolitan municipality has a negative impact, however this result is not statistically significant. As for political power, this does not show any connection with the dependent variable on new arrivals. It can be explained by using the median voter theorem which explains how the parties attract the greatest number of voters.   Keywords: Refugee policy, Refugee, Municipality, Regression analysis. / Under flyktingkrisen i Sverige 2015 blev det påtagligt att det finns brister i det svenska mottagningssystemet, det uppmärksammades att nyanlända inte fördelas jämnt mellan landets kommuner. En jämn fördelning är enligt Sveriges kommuner och landsting (SKL) viktig för ett lyckat mottagande och integration. Uppsatsens syfte är att undersöka frågan: ”Vad kan den ojämna fördelningen av nyanlända mellan landets kommuner bero på?” För att analysera frågeställningen används i uppsatsen sekundärdata mellan 2006–2015, metoden som används är poolade regressioner och minsta kvadratdummyvariabel (LSDV) modellen. Data samlas över sådana variabler som bör ha ett samband med den beroende variabeln nyanlända/capita utifrån tidigare studier samt teorier. Utöver valda variabler är vi medvetna om att det finns fler saker som kan påverka kommuners mottagande men som inte mäts i denna studie. Studien finner att ett antal av de undersökta variablerna har ett samband med antalet nyanlända som mottages i en kommun.  Andel tidigare utlandsfödda i kommunen har ett positivt samband med andel nyanlända vilket kan bero på att många nyanlända bosätter sig i områden där det finns personer med liknande bakgrund. Medelinkomsten i en kommun visar sig ha ett negativt samband till den beroende variabeln vilket möjligen beror på att personer med liknande inkomst bosätter sig i liknande områden. Utbildningsnivå är också negativt relaterat till andel nyanlända som mottages i en och kan motiveras på liknande sätt som medelinkomst. Andel pensionärer i en kommun har ett positivt samband med den beroende variabeln vilket är ett oväntat resultat. Arbetslöshet har också ett positivt samband med andelen nyanlända men resultaten är otydliga. Bostadsunderskott har ett negativt samband med den beroende variabeln medan bostadsöverskott har ett positivt samband vilket var väntat. Landsbygdskommuner visar ett positivt samband som är statistiskt säkerställt, storstadskommun har ett negativt samband men resultatet är inte signifikant. Politiskt styre visar inte något samband till den beroende variabeln nyanlända per capita vilket kan förklaras med hjälp av medianväljarteoremet.   Nyckelord: Flyktingmottagande, nyanlända, kommuner, regressionsanalys.

Master of Science / Department of Biological and Agricultural Engineering / Naiqian Zhang / Permittivity, displayed when a dielectric material is exposed to an electric field, is a useful property for measuring impurities in a dielectric medium. These impurities often have a dipole moment different from the pure material, and the dipoles align through polarization and impede electric current. By measuring the resulting impedance in a known geometry, the permittivity can be determined. Four permittivity sensors were utilized to measure contaminants that are associated with biofuels, specifically glycerol, ethanol, and ammonia. These sensors were based around either stainless steel or aluminum plates to ensure durability and reliability. By connecting each of these sensors to a signal generating control box, the gain and phase can be measured at 609 frequencies, from 10 kHz up to 120 MHz. Data from each of the three contaminants were run through a method for detection. Measurements for ambient air and air with the contaminants were compared with a statistical analysis. Glycerol, ethanol, and ammonia each had significantly different measurements in the gain and phase data at a unique set of frequencies. Using a neural network analysis for detection resulted in a 95.8%, 93.9%, and 97.1% success rate for detecting glycerol, ethanol, and ammonia, respectively. For ethanol and ammonia, where multiple concentrations were measured, regression methods were used to relate the frequency response data to the contaminant concentration. Stepwise regression, wavelet transformation followed by stepwise regression, partial least squares regression, and neural network regression were the four methods used to establish these relationships. Several regressions over-fit the data, showing coefficient of determination (R[superscript]2) values of 1.000 for training data, yet very low R[superscript]2 values for validation data. However, the best R[superscript]2 values of all the regressions were 1.000 and 0.996 for the training and validation data, respectively, from measuring ammonia.

Orientador: Luzia Aparecida Trinca / Banca: Rogério Antonio de Oliveira / Banca: Josmar Mazucheli / Resumo: Neste trabalho, a flexibilidade dos modelos de polinômios fracionários foi explorada como alternativa quando polinômios simples mostram falta de ajuste em modelos de regressão. Vários tipos de modelos de regressão foram considerados incluindo regressão logística, regressão logística com efeitos aleatórios, e regressão para resposta quantitativa contínua com aumento de variância. Três aplicações para dados biológicos foram realizadas: o exemplo dos rotíferos apresentado em Collett (1991), o estudo da tolerância para temperaturas de células de fungos apresentado em Theodoro et al. (2008) e o estudo da relação entre peso e comprimento de uma espécie de aranhas considerado em Stropa & Trinca (2005). Nos dois primeiros exemplos o modelo de regressão logística foi considerado e o ajuste mostrou sobredispersão dos dados, bem como falta de ajuste dos modelos simples. O uso de polinômios fracionários e a inclusão de efeitos aleatórios nas funções dos preditores lineares mostraram benefícios para ambos os problemas de modelagem. No terceiro exemplo, a relação entre a variável resposta e a regressora foi não-linear com variância do erro não constante. O uso simultâneo de polinômios fracionários e transformações do tipo Box-Cox resultaram em funções preditivas razoáveis para o problema. A influência de pontos particulares foi explorada e todos os exemplos ilustraram que o processo de modelagem, na prática, requer cuidados nas inspeções das violações do modelo, considerações do problema em particular, e na tomada de decisões / Abstract: In this work the flexibility of fractional polynomial models were explored as alternative when simple polynomials show lack of fit in regression models. Several types of regression models were considered including logistic regression, mixed logistic regression, and regression for a continuous quantitative response with increasing variance. Three applications to biological data were shown: the rotifers example of Collett (1991); the study of tolerance to temperature of fungus cells of Theodoro et al. (2008); and the study of the relationship between weight and size of a specie of spiders of Stropa & Trinca (2005). In the first two examples the logistic model was considered and overdispersion as well as lack of fit of simple models were detected. The use of fractional polynomials and the inclusion of random effects in the linear predictor function showed benefits to both modeling problems. In the third example the relation was non-linear with nonconstant error variance. The simultaneous use of fractional polynomials and Box-Cox transformation resulted in very reasonable prediction functions. Influence of particular points were explored. All examples illustrated that the modeling process in practice includes careful inspections of model violations, practical considerations and decisions / Mestre

In microarray analysis, people are interested in those features that have different characters in diseased samples compared to normal samples. The usual p-value method of selecting significant genes either gives too many false positives or cannot detect all the significant features. The False Discovery Rate (FDR) method controls false positives and at the same time selects significant features. We introduced Benjamini's method and Storey's method to control FDR, applied the two methods to human Meningioma data. We found that Benjamini's method is more conservative and that, after the number of the tests exceeds a threshold, increase in number of tests will lead to decrease in number of significant genes. In the second chapter, we investigate ways to search interesting gene expressions that cannot be detected by linear models as t-test or ANOVA. We propose a novel approach to use quadratic logistic regression to detect genes in Meningioma data that have non-linear relationship within phenotypes. By using quadratic logistic regression, we can find genes whose expression correlates to their phenotypes both linearly and quadratically. Whether these genes have clinical significant is a very interesting question, since these genes most likely be neglected by traditional linear approach.

Background: Feature selection, also known as variable selection, is a technique that selects a subset from a large collection of possible predictors to improve the prediction accuracy in regression model. First objective of this project is to investigate in what data structure LASSO outperforms forward stepwise method. The second objective is to develop a feature selection method, Feature Selection by L1 Regularization of Subset of Parameters (LRSP), which selects the model by combining prior knowledge of inclusion of some covariates, if any, and the information collected from the data. Mathematically, LRSP minimizes the residual sum of squares subject to the sum of the absolute value of a subset of the coefficients being less than a constant. In this project, LRSP is compared with LASSO, Forward Selection, and Ordinary Least Squares to investigate their relative performance for different data structures. Results: simulation results indicate that for moderate number of small sized effects, forward selection outperforms LASSO in both prediction accuracy and the performance of variable selection when the variance of model error term is smaller, regardless of the correlations among the covariates; forward selection also works better in the performance of variable selection when the variance of error term is larger, but the correlations among the covariates are smaller. LRSP was shown to be an efficient method to deal with the problems when prior knowledge of inclusion of covariates is available, and it can also be applied to problems with nuisance parameters, such as linear discriminant analysis.

For a segmented regression system with an unknown change-point over two domains of a predictor, a new empirical likelihood ratio test statistic is proposed to test the null hypothesis of no change. The proposed method is a non-parametric method which releases the assumption of the error distribution. Under the null hypothesis of no change, the proposed test statistic is shown empirically Gumbel distributed with robust location and scale parameters under various parameter settings and error distributions. Under the alternative hypothesis with a change-point, the comparisons with two other methods (Chen's SIC method and Muggeo's SEG method) show that the proposed method performs better when the slope change is small. A power analysis is conducted to illustrate the performance of the test. The proposed method is also applied to analyze two real datasets: the plasma osmolality dataset and the gasoline price dataset. / by Zhihua Liu. / Thesis (Ph.D.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 200?. Mode of access: World Wide Web.

Neste trabalho foi realizada uma revisão bibliográfica acerca das metodologias encontradas na literatura de como são aplicados os métodos para o uso analítico de dados provenientes de pesquisas que envolvem esquemas amostrais complexos. Objetivou-se mostrar e discutir alguns estudos que avaliam o impacto de ignorar o plano amostral na análise dos dados. Foi feito também um levantamento de artigos com o objetivo de fazer um estudo de trabalhos publicados em jornais, revistas ou periódicos, cujos assuntos abordados tratam da incorporação da estrutura complexa da amostra na análise. Essa revisão evidenciou que os métodos clássicos de análise, ou seja, aqueles que supõem que os dados provém de uma amostragem aleatória simples, podem levar a resultados incorretos produzindo conclusões errôneas ou equivocadas quando os dados provém de esquemas amostrais complexos. / This work was carried out a literature review about the methodologies found in the literature of how the methods for data analytical use from research involving complex sampling schemes are applied. It was aimed to show and discuss some studies that assess the impact of ignoring the sampling scheme in the data analysis. It was also made a survey of articles in order to make a study of works published in newspapers, magazines or periodicals, which addressed issues dealing with the incorporation of the complex structure of the sample in the analysis. This review shown that the classical methods of analysis, i.e. those who assume that the data comes from a simple random sampling can lead to incorrect results producing quite erroneous and misleading conclusions when the data come from complex sample schemes.

Este trabalho tem o objetivo de divulgar a metodologia de ponderação de modelos ou Bayesian Model Averaging (BMA) entre os pesquisadores da área agronômica e discutir suas vantagens e limitações. Com o BMA é possível combinar resultados de diferentes modelos acerca de determinada quantidade de interesse, com isso, o BMA apresenta-se como sendo uma metodologia alternativa de análise de dados frente os usuais métodos de seleção de modelos tais como o Coeficiente de Determinação Múltipla (R2 ), Coeficiente de Determinação Múltipla Ajustado (R2), Estatística de Mallows ( Cp) e Soma de Quadrados de Predição (PRESS). Vários trabalhos foram, recentemente, realizados com o objetivo de comparar o desempenho do BMA em relação aos métodos de seleção de modelos, porém, há ainda muitas situações para serem exploradas até que se possa chegar a uma conclusão geral acerca desta metodologia. Neste trabalho, o BMA foi aplicado a um conjunto de dados proveniente de um experimento agronômico. A seguir, o desempenho preditivo do BMA foi comparado com o desempenho dos métodos de seleção acima citados por meio de um estudo de simulação variando o grau de multicolinearidade e o tamanho amostral. Em cada uma dessas situações, foram utilizadas 1000 amostras geradas a partir de medidas descritivas de conjuntos de dados reais da área agronômica. O desempenho preditivo das metodologias em comparação foi medido pelo Logaritmo do Escore Preditivo (LEP). Os resultados empíricos obtidos indicaram que o BMA apresenta desempenho semelhante aos métodos usuais de seleção de modelos nas situações de multicolinearidade exploradas neste trabalho. / The objective of this work was divulge to Bayesian Model Averaging (BMA) between the researchers of the agronomy area and discuss its advantages and limitations. With the BMA is possible combine results of difeerent models about determined quantity of interest, with that, the BMA presents as being a metodology alternative of data analysis front the usual models selection approaches, for example the Coefficient of Multiple Determination (R2), Coefficient of Multiple Determination Adjusted (R2), Mallows (Cp Statistics) and Prediction Error Sum Squares (PRESS). Several works recently were carried out with the objective of compare the performance of the BMA regarding the approaches of models selection, however, there is still many situations for will be exploited to that can arrive to a general conclusion about this metodology. In this work, the BMA was applied to data originating from an agronomy experiment. It follow, the predictive performance of the BMA was compared with the performance of the approaches of selection above cited by means of a study of simulation varying the degree of multicollinearity, measured by the number of condition of the matrix standardized X'X and the number of observations in the sample. In each one of those situations, were utilized 1000 samples generated from the descriptive information of agronomy data. The predictive performance of the metodologies in comparison was measured by the Logarithm of the Score Predictive (LEP). The empirical results obtained indicated that the BMA presents similar performance to the usual approaches of selection of models in the situations of multicollinearity exploited.

Kwan Ka Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references. / Abstracts in English and Chinese. / Chapter 1. --- Introduction --- p.1 / Chapter 1.1. --- Automatic Speech Recognition --- p.1 / Chapter 1.2. --- Robustness of ASR Systems --- p.3 / Chapter 1.3. --- Model Adaptation for Robust ASR --- p.4 / Chapter 1.4. --- Thesis outline --- p.6 / References --- p.8 / Chapter 2. --- Fundamentals of Continuous Speech Recognition --- p.10 / Chapter 2.1. --- Acoustic Front-End --- p.10 / Chapter 2.2. --- Recognition Module --- p.11 / Chapter 2.2.1. --- Acoustic Modeling with HMM --- p.12 / Chapter 2.2.2. --- Basic Phonology of Cantonese --- p.14 / Chapter 2.2.3. --- Acoustic Modeling for Cantonese --- p.15 / Chapter 2.2.4. --- Language Modeling --- p.16 / References --- p.17 / Chapter 3. --- Unsupervised Model Adaptation --- p.18 / Chapter 3.1. --- A General Review of Model Adaptation --- p.18 / Chapter 3.1.1. --- Supervised and Unsupervised Adaptation --- p.20 / Chapter 3.1.2. --- N-Best Adaptation --- p.22 / Chapter 3.2. --- MAP --- p.23 / Chapter 3.3. --- MLLR --- p.25 / Chapter 3.3.1. --- Adaptation Approach --- p.26 / Chapter 3.3.2. --- Estimation of MLLR regression matrices --- p.27 / Chapter 3.3.3. --- Least Mean Squares Regression --- p.29 / Chapter 3.3.4. --- Number of Transformations --- p.30 / Chapter 3.4. --- Experiment Results --- p.32 / Chapter 3.4.1. --- Standard MLLR versus LMS MLLR --- p.36 / Chapter 3.4.2. --- Effect of the Number of Transformations --- p.43 / Chapter 3.4.3. --- MAP Vs. MLLR --- p.46 / Chapter 3.5. --- Conclusions --- p.48 / Referencesxlix / Chapter 4. --- Use of Confidence Measure for MLLR based Adaptation --- p.50 / Chapter 4.1. --- Introduction to Confidence Measure --- p.50 / Chapter 4.2. --- Confidence Measure Based on Word Density --- p.51 / Chapter 4.3. --- Model-level confidence measure --- p.53 / Chapter 4.4. --- Integrating Confusion Information into Confidence Measure --- p.55 / Chapter 4.5. --- Adaptation Data Distributions in Different Confidence Measures..… --- p.57 / References --- p.65 / Chapter 5. --- Experimental Results and Analysis --- p.66 / Chapter 5.1. --- Supervised Adaptation --- p.67 / Chapter 5.2. --- Cheated Confidence Measure --- p.69 / Chapter 5.3. --- Confidence Measures of Different Levels --- p.71 / Chapter 5.4. --- Incorporation of Confusion Matrix --- p.81 / Chapter 5.5. --- Conclusions --- p.83 / Chapter 6. --- Conclusions --- p.35 / Chapter 6.1. --- Future Works --- p.88

Yiu Tat-choi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 68-69). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Student Ratings of Instructors --- p.2 / Chapter 1.2 --- Research Plan and Difficulties Encountered in the Study --- p.4 / Chapter 2 --- Data and An Overall Picture of Study --- p.7 / Chapter 2.1 --- The Questionnaire and Data Collection Method --- p.7 / Chapter 2.2 --- Pilot Study --- p.8 / Chapter 2.3 --- Data Editing --- p.12 / Chapter 2.3.1 --- Clerical Error --- p.12 / Chapter 2.3.2 --- Strange Patterns --- p.13 / Chapter 2.4 --- Missing Items ´ؤ Item Nonresponse --- p.14 / Chapter 2.5 --- Missing Items - Unit Nonresponse --- p.16 / Chapter 2.6 --- Effective Class Size --- p.21 / Chapter 2.7 --- Imputation of Item Nonresponse Data --- p.23 / Chapter 2.8 --- Overall Picture of Study --- p.25 / Chapter 3 --- Data Analysis I: Logistic Regression --- p.28 / Chapter 3.1 --- Conditional Independence --- p.29 / Chapter 3.2 --- Partial Correlation --- p.30 / Chapter 3.3 --- Simultaneous p-value --- p.31 / Chapter 3.4 --- Logit Model --- p.32 / Chapter 3.5 --- Logit Model for Ordinal Variables --- p.35 / Chapter 3.6 --- Iteratively Reweighted Least Squares (IRLS) Algorithm --- p.36 / Chapter 3.7 --- Criteria for Assessing Model Fit --- p.38 / Chapter 3.7.1 --- Assessing the Fit of the Model --- p.39 / Chapter 3.7.2 --- Pearson Chi-Square and Deviance --- p.40 / Chapter 3.8 --- Interpretation of the Coefficients of The Weighted Logistic Re- gression Model --- p.42 / Chapter 3.8.1 --- Nominal Independent Variable --- p.42 / Chapter 3.8.2 --- Continuous Independent Variable --- p.45 / Chapter 4 --- Data Analysis II: Adjusted Instructor Score --- p.49 / Chapter 4.1 --- Removing Effects of Class Characteristics Factor and Adjust- ing the Score --- p.50 / Chapter 4.2 --- Adjusted Instructor Score (AIS) --- p.54 / Chapter 4.3 --- Estimate Standard Error of AIS by Bootstrap Method --- p.55 / Chapter 5 --- Conclusion --- p.58 / Chapter 5.1 --- Comparison Between the AIS and Average Score --- p.58 / Chapter 5.2 --- Discussion --- p.60 / Appendix A1: Course Evaluation Survey Form --- p.63 / Appendix A2: Course Evaluation Supplementary Form . --- p.64 / Appendix B: Descriptive Statistics for Response Rate --- p.65 / Appendix C: The Descriptions of Class Characteristics Dummy Variables --- p.67 / Bibliography --- p.68